Dual-resource constrained flexible job shop scheduling with grey processing time and worker collaborative learning effect

作者：Nanlei Chen, Jilei Shi, K. X. Zhang · 发表于：Grey Systems Theory and Application · 年份：2026 · DOI：10.1108/gs-01-2026-0009 · 被引用次数：1 · 研究领域：Scheduling and Optimization Algorithms、Resource-Constrained Project Scheduling、Scheduling and Timetabling Solutions

Purpose This study addresses a dual-resource constrained flexible job shop scheduling problem (DRCFJSP) in the context of Industry 5.0, where both machines and workers must be coordinated. It incorporates a team-based collaborative learning effect that dynamically enhances worker proficiencies and uses interval grey numbers to represent uncertain processing times. The aim is to optimize makespan, maximum team workload, and total worker proficiency improvement simultaneously. Design/methodology/approach A multi-objective grey dual-resource constrained flexible job shop model is proposed, integrating interval grey numbers to represent uncertainties and a collaborative learning effect to reflect skill evolution. To solve this problem, a knowledge-guided multi-objective evolutionary algorithm (KGMOEA) is designed, featuring a four-layer encoding scheme and domain-specific strategies such as knowledge-guided population initialization and neighborhood search. Findings The proposed algorithm outperforms benchmark methods in convergence and diversity. Incorporating collaborative learning effects reduces total completion time and improves resource allocation efficiency. Experimental results confirm that the method effectively balances production efficiency with workforce skill development under uncertainty. Practical implications This paper provides a practical scheduling framework for high-complexity manufacturing environments, such as aviation composite workshops, where human–machin...